[In a vector database] Data points are stored as arrays of numbers called “vectors,” which are clustered based on similarity. This design enables low-latency queries, making it ideal for AI applications.
Visualizing how similar vectors are stored and searched
Vector databases store and search data using vectors — numerical representations of things like text, images, or audio. These databases are designed to find similar items based on meaning, not just exact matches.
Let’s say you searched for “best places to visit in summer.” A traditional database might look for exact text matches. But a vector database looks for entries semantically similar to your query, even if they use totally different words — like “top vacation destinations for warm weather.”
Why does this matter? It’s the backbone of Retrieval-Augmented Generation (RAG) systems, which feed relevant context into language models like ChatGPT. When you ask a question, the system:
- Turns your query into a vector
- Searches the vector database for similar content
- Feeds that content into the model so it can respond with grounded answers
Key Features:
- Finds similar items using cosine or Euclidean distance
- Handles high-dimensional vectors from models like OpenAI, Cohere, or Hugging Face
- Extremely fast at scale with billions of entries
Real-World Use Cases:
- Powering AI search (like in Notion AI or ChatGPT plugins)
- Personalized recommendations (e.g., similar songs, movies, products)
- RAG pipelines for grounding LLM responses
It’s like Google Search — but for meaning, not just words.
FAQ
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Related Stuff
- Retrieval Augmented Generation: How LLMs get extra brainpower from external documents
- Embeddings: The vectors that represent meaning
- Vector Similarity Search: How databases find ‘close’ matches, not exact ones
- Semantic Search: Searching by meaning, not keywords